IoMT Meets Machine Learning: From Edge to Cloud Chronic Diseases Diagnosis System.

Autor: Nigar N; Department of Computer Science (RCET), University of Engineering and Technology, Lahore, Pakistan., Jaleel A; Department of Computer Science (RCET), University of Engineering and Technology, Lahore, Pakistan., Islam S; Department of Computer Science (RCET), University of Engineering and Technology, Lahore, Pakistan., Shahzad MK; Power Information Technology Company (PITC), Ministry of Energy,Power Division, Government of Pakistan, Lahore, Pakistan., Affum EA; Department of Telecommunication Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Jazyk: angličtina
Zdroj: Journal of healthcare engineering [J Healthc Eng] 2023 Jun 01; Vol. 2023, pp. 9995292. Date of Electronic Publication: 2023 Jun 01 (Print Publication: 2023).
DOI: 10.1155/2023/9995292
Abstrakt: In conventional healthcare, real-time monitoring of patient records and information mining for timely diagnosis of chronic diseases under certain health conditions is a crucial process. Chronic diseases, if not diagnosed in time, may result in patients' death. In modern medical and healthcare systems, Internet of Things (IoT) driven ecosystems use autonomous sensors to sense and track patients' medical conditions and suggest appropriate actions. In this paper, a novel IoT and machine learning (ML)-based hybrid approach is proposed that considers multiple perspectives for early detection and monitoring of 6 different chronic diseases such as COVID-19, pneumonia, diabetes, heart disease, brain tumor, and Alzheimer's. The results from multiple ML models are compared for accuracy, precision, recall, F1 score, and area under the curve (AUC) as a performance measure. The proposed approach is validated in the cloud-based environment using benchmark and real-world datasets. The statistical analyses on the datasets using ANOVA tests show that the accuracy results of different classifiers are significantly different. This will help the healthcare sector and doctors in the early diagnosis of chronic diseases.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2023 Natasha Nigar et al.)
Databáze: MEDLINE
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